Share Email Print
cover

Proceedings Paper

Level-set segmentation of pulmonary nodules in radiographs using a CT prior
Author(s): Jay S. Schildkraut; Shoupu Chen; Michael Heath; Walter G. O'Dell; Paul Okunieff; M. C. Schell; Narinder Paul
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

This research addresses the problem of determining the location of a pulmonary nodule in a radiograph with the aid of a pre-existing computed tomographic (CT) scan. The nodule is segmented in the radiograph using a level set segmentation method that incorporates characteristics of the nodule in a digitally reconstructed radiograph (DRR) that is calculated from the CT scan. The segmentation method includes two new level set energy terms. The contrast energy seeks to increase the contrast of the segmented region relative to its surroundings. The gradient direction convergence energy is minimized when the intensity gradient direction in the region converges to a point. The segmentation method was tested on 23 pulmonary nodules from 20 cases for which both a radiographic image and CT scan were collected. The mean nodule effective diameter is 22.5 mm. The smallest nodule has an effective diameter of 12.0 mm and the largest an effective diameter of 48.1 mm. Nodule position uncertainty was simulated by randomly offsetting the true nodule center from an aim point. The segmented region is initialized to a circle centered at the aim point with a radius that is equal to the effective radius of the nodule plus a 10.0 mm margin. When the segmented region that is produced by the proposed method is used to localize the nodule, the average reduction in nodule-position uncertainty is 46%. The relevance of this method to the detection of radiotherapy targets at the time of treatment is discussed.

Paper Details

Date Published: 27 March 2009
PDF: 14 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72593B (27 March 2009); doi: 10.1117/12.808288
Show Author Affiliations
Jay S. Schildkraut, Carestream Health, Inc. (United States)
Shoupu Chen, Carestream Health, Inc. (United States)
Michael Heath, Carestream Health, Inc. (United States)
Walter G. O'Dell, Univ. of Rochester Medical Ctr. (United States)
Paul Okunieff, Univ. of Rochester Medical Ctr. (United States)
M. C. Schell, Univ. of Rochester Medical Ctr. (United States)
Narinder Paul, Toronto General Hospital (Canada)


Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

© SPIE. Terms of Use
Back to Top